How to test for outliers in an mlogit model in R I am running a multinomial logistic regression using the mlogit package and mlogit function in R.  Now I need to check for outliers for the model.
Is there any approach or function in R for testing outliers in an mlogit model?
 A: I assume that what you want is a diagnostic plot of some sort that examines residuals against fitted values. Typically model outliers are observations whose fitted values $\hat{y}$ are very different from their observed values $y$. In other words, they have an abnormally large residual $\epsilon = y - \hat{y}$.
The trick is that multinomial logit models rely on a latent, unobserved $y^*$ instead of $y$. So the entire model is based on the assumption that the error terms have an independent and identical extreme value distribution, an assumption that doesn't leave room for the concept of an "outlier." If you think your data are not IID-EV, you should use a different model. 
Also, remember that the ultimate output of an MNL model is a probability; just because you observe someone "choosing" a category without the highest probability doesn't mean you have an "outlier." 
Having said all of this, you can still do a leverage points analysis to determine if some observations are unique to the extent that they can affect your likelihood estimates.
